Which solution will result in the MOST cost savings?

Explanations:

Changing to a memory-optimized instance with the same vCPU but not leveraging the cost-efficient SageMaker Processing service for data preprocessing will not significantly reduce costs. While stopping the notebook when not in use is good practice, the instance type change alone does not fully address the high cost from running a large instance continuously.

Keeping the current notebook instance type and size means the company will continue to incur high costs from the ml.m5.4xlarge instance. Running the preprocessing on a P3 instance is unnecessary since the data processing does not require GPU acceleration and is more expensive than a memory-optimized instance. Thus, this option fails to achieve cost savings.

Changing to a smaller general-purpose instance for the notebook will save costs while still allowing for feature engineering. Utilizing an ml.r5 instance for data preprocessing, which is memory-optimized, provides the required memory without incurring high costs. The processing job can be run only when needed, further optimizing costs.

While changing to a smaller general-purpose instance for the notebook and stopping it when not in use can save costs, using an R5 instance with the Reserved Instance option is not the best approach. Reserved Instances require a long-term commitment and might not be needed if the preprocessing only occurs once a day, making this option less flexible and potentially more costly in the short term.